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      <title>Presentation: The Multi-Agent Approach: Building Reliable and Controllable Software Development Automation</title>
      <link>https://www.infoq.com/presentations/multi-agent-ai-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Programming-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/multi-agent-ai-architecture/en/mediumimage/itamar-friedman-medium-1782818996066.jpeg"/&gt;&lt;p&gt;Itamar Friedman discusses how architects and engineering leaders can break through the AI productivity ceiling using adaptive multi-agent systems. He shares insights on moving past simple autocomplete to resilient workflows by integrating autonomous testing, intelligent code review, and robust arbitration. Learn how to govern agent communication and build a context-driven SDLC that scales.&lt;/p&gt; &lt;i&gt;By Itamar Friedman&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>QCon AI 2025</category>
      <category>Transcripts</category>
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      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Wed, 08 Jul 2026 14:06:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/multi-agent-ai-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Programming-presentations</guid>
      <dc:creator>Itamar Friedman</dc:creator>
      <dc:date>2026-07-08T14:06:00Z</dc:date>
      <dc:identifier>/presentations/multi-agent-ai-architecture/en</dc:identifier>
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    <item>
      <title>Presentation: Designing AI Platforms for Reliability: Tools for Certainty, Agents for Discovery</title>
      <link>https://www.infoq.com/presentations/reliable-ai-platforms/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Programming-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/reliable-ai-platforms/en/mediumimage/aaron-erickson-medium-1782819611516.jpg"/&gt;&lt;p&gt;Aaron Erickson explains how NVIDIA designs and tests purpose-built AI agent hierarchies. For senior developers and architects, he outlines why balancing deterministic tools with agentic discovery is crucial. Discover how to leverage rare context, implement LLM-as-a-judge test pyramids, and avoid the paradox of choice to build highly reliable, production-grade AI systems at scale.&lt;/p&gt; &lt;i&gt;By Aaron Erickson&lt;/i&gt;</description>
      <category>QCon San Francisco 2025</category>
      <category>Model</category>
      <category>Artificial Intelligence</category>
      <category>Reliability</category>
      <category>Transcripts</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Tue, 07 Jul 2026 08:03:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/reliable-ai-platforms/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Programming-presentations</guid>
      <dc:creator>Aaron Erickson</dc:creator>
      <dc:date>2026-07-07T08:03:00Z</dc:date>
      <dc:identifier>/presentations/reliable-ai-platforms/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Practical Robustness: Going beyond Memory Safety in Rust</title>
      <link>https://www.infoq.com/presentations/rust-autonomous-mobile-robots/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Programming-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/rust-autonomous-mobile-robots/en/mediumimage/andy-brinkmeyer-medium-1782817486635.jpeg"/&gt;&lt;p&gt;Andy Brinkmeyer shares how engineering leaders and architects can use Rust to build failure-proof systems. Moving beyond memory safety, he explains how ownership, enums, and the typestate pattern embed complex runtime protocols into compile-time checks. Learn to eliminate entire classes of bugs, manage real-world resources safely, and maximize codebase robustness effortlessly.&lt;/p&gt; &lt;i&gt;By Andy Brinkmeyer&lt;/i&gt;</description>
      <category>InfoQ Dev Summit Munich 2025</category>
      <category>Rust</category>
      <category>Reliability</category>
      <category>Transcripts</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Mon, 06 Jul 2026 09:01:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/rust-autonomous-mobile-robots/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Programming-presentations</guid>
      <dc:creator>Andy Brinkmeyer</dc:creator>
      <dc:date>2026-07-06T09:01:00Z</dc:date>
      <dc:identifier>/presentations/rust-autonomous-mobile-robots/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Fine Tuning the Enterprise: Reinforcement Learning in Practice</title>
      <link>https://www.infoq.com/presentations/rft-openai-model/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Programming-presentations</link>
      <description>&lt;img src="https://res.infoq.com/presentations/rft-openai-model/en/mediumimage/WenjieZiWillHang-medium-1782220624463.jpg"/&gt;&lt;p&gt;The speakers discuss Agent RFT, OpenAI’s platform for fine-tuning reasoning models via real-time tool interactions and custom reward signals. They explain how reinforcement learning solves complex credit assignment challenges within the context window. They share enterprise success stories, showing how Agent RFT eliminates long-tail token loops and drives extreme efficiency.&lt;/p&gt; &lt;i&gt;By Wenjie Zi, Will Hang&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>QCon AI 2025</category>
      <category>Large language models</category>
      <category>Transcripts</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Fri, 03 Jul 2026 09:22:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/rft-openai-model/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Programming-presentations</guid>
      <dc:creator>Wenjie Zi, Will Hang</dc:creator>
      <dc:date>2026-07-03T09:22:00Z</dc:date>
      <dc:identifier>/presentations/rft-openai-model/en</dc:identifier>
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